This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Recent developments in MR data acquisition technology, in part driven by the advances in the types of phased-arrays developed by Project 2, are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify hippocampal subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical researchers. In the current cycle of the P41 we are in the process of developing a fully-automated method for segmenting the hippocampal subfields from ultra-high resolution MRI data. Using a Bayesian approach, we build a computational model of how images around the hippocampus are generated, and use this model to obtain automated segmentations. We validate the proposed technique by comparing our segmentation results with corresponding manual delineations in ultra-high resolution MRI scans of five individuals.